highFIS
highFIS is a PyTorch-based library for high-dimensional Takagi–Sugeno–Kang (TSK) fuzzy systems. It brings differentiable fuzzy inference, numerical stability, and scikit-learn compatible estimators to both classification and regression.
🚀 Overview
- Differentiable TSK fuzzy systems built for high-dimensional data.
- Thirteen model families from the literature behind one API, each with a classifier and a regressor.
- Available as scikit-learn compatible estimators and as plain PyTorch model classes.
- Designed for numerical stability with log-space and inverse-log defuzzifiers.
📦 Installation
Install from PyPI:
pip install highfis
highFIS requires Python 3.11 or newer and depends on PyTorch, NumPy, scikit-learn, and tqdm.
🧠 Quick Start
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from highfis import HTSKClassifier
X, y = make_classification(n_samples=800, n_features=10, n_informative=8, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
# Membership functions are sensitive to feature scale.
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
clf = HTSKClassifier(
n_mfs=3,
mf_init="kmeans",
epochs=100,
learning_rate=0.01,
random_state=42,
)
clf.fit(X_train, y_train)
print(f"Test accuracy: {clf.score(X_test, y_test):.2%}")
highFIS integrates with sklearn.pipeline.Pipeline, GridSearchCV, and
cross_val_score.
🧩 Model families
highFIS implements thirteen TSK model families, each following a published high-dimensional inference strategy.
TSK— vanilla TSK with product antecedent aggregation and sum-based normalization.HTSK— geometric mean aggregation with log-space softmax normalization.LogTSK— inverse-log normalization of log-domain rule weights.HDFIS— high-dimensional inference with product T-norm (HDFISProd) and minimum T-norm (HDFISMin) variants.DombiTSK— Dombi T-norm aggregation with a fixed shape parameter.ADMTSK— adaptive Dombi T-norm, with the shape parameter derived from the input dimensionality, and composite Gaussian membership functions.AYATSK— adaptive Yager T-norm with composite exponential membership functions.ADATSK— adaptive softmin antecedent aggregation.ADPTSK— adaptive double-parameter softmin antecedent aggregation.FSRE-ADATSK— ADATSK extended with embedded feature selection and rule extraction.DGTSK— double groups of gates for simultaneous feature selection and rule extraction.DGALETSK— adaptive Ln-Exp softmin with simultaneous feature selection and rule extraction.MHTSK— multihead TSK built from sparse subantecedents over random feature subsets.
Each family exposes classifier and regressor variants.
🔧 Core components
highFIS exposes a compact, model-family-driven API with both concrete model classes and sklearn-compatible estimator wrappers.
- Estimators:
*Classifierand*Regressorvariants for each model family, importable directly fromhighfis(for exampleHTSKClassifier,FSREADATSKRegressor,HDFISProdClassifier) - PyTorch models: the underlying
*ClassifierModeland*RegressorModelclasses inhighfis.models - Building blocks: membership functions (
highfis.memberships), defuzzifiers (highfis.defuzzifiers), T-norms (highfis.t_norms), and layers (highfis.layers) - Utilities: evaluation metrics (
highfis.metrics), estimator checkpoints (highfis.persistence), and a membership-function initialization cache
For the full class list and API reference, see the documentation:
🛠️ Training options
highFIS uses gradient-based optimization and supports:
- an optimizer selected per model family (SGD, Adam, or AdamW), following the source paper
- mini-batch training with learning-rate schedulers and weight decay on the consequent parameters
- early stopping on a validation set passed to
fit - uniform regularization (
ur_weight) for balanced rule activation - custom T-norms, rule bases, and defuzzifiers through the PyTorch model classes
📚 Documentation
The published documentation is available at:
https://dcruzf.github.io/highFIS
Start with the model families overview, the user guides, and the cookbook.
Model reference pages:
- TSK Vanilla
- LogTSK
- AYATSK
- HTSK
- DombiTSK
- ADPTSK
- ADMTSK
- ADATSK
- DGTSK
- DG-ALETSK
- FSRE-ADATSK
- MHTSK
- HDFIS
🧪 Testing & quality
Format, lint, and type check:
hatch check --fix
Run the test suite with coverage:
hatch test -c -a
Run security scan:
hatch run security
📝 Citation
If you use highFIS in your research, please cite it using the metadata in CITATION.cff or the Zenodo DOI.
🤝 Contributing
Contributions are welcome! Please open issues or pull requests, and refer to our development guide in the documentation: contributing.
When reporting a bug, include the output of highfis.show_versions().
📄 License
Distributed under the GPLv3.
1 maintainer
Metadata
1 maintainer
Release files for highfis 0.30.0
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